Radio frequency spectrum is becoming increasingly disaggregated. This is due to a number of factors, including the historical pattern in which various bands of spectrum have been allocated to various commercial and/or military interests. The growth and proliferation of various digital communications have placed increasing pressure on the efficient use of spectrum. The tremendous growth of this industry sector has made the robustness and reliability of wideband communications an increasing challenge. Further, the proliferation of communications devices and the relative increase in data rates has generated an increasing amount of data that needs to be transmitted over a reduced amount of spectrum.
These problems are exacerbated by multi-path complications, as well as various terrain features. In addition, the number of competing systems has increased. The physical range between systems has decreased, and the susceptibility of various communications to interference from other systems has increased. Further complicating this scenario is the tremendous proliferation of mobile devices and communication systems, both in the civilian and military sectors. This has produced a number of technical challenges.
In the field of radar signal processing, a technique known as “pulse compression” has long been used to improve the range resolution of radars. In general, pulse compression involves modulating a transmitted radar pulse (i.e., with a “code” or “waveform”) and then correlating the received signal with an appropriate “filter” function, based on the known modulation. This technique can also be applied to communication systems to create waveforms that effectively make use of fragmented spectrum, maintain or increase data rates, and change in accordance to varying spectral conditions.
One reason for implementing a pulse compression system is to obtain the high range resolution of a short pulse, while realizing the higher signal-to-noise ratio (SNR) of a longer uncoded pulse. This is accomplished by increasing the bandwidth of the longer pulse by introducing signal modulation. This technique can extend the maximum detectable range, improve the probability of detection (PD), and affect a lower probability of intercept (LPI), by lowering the peak power requirements for the same SNR. In a communication system, however, pulse compression is used to control the data rate by manipulating the time and spectral properties of the signal.
A major principle in radar is the coherent combination of signals in order to affect what signals are seen and what signals are suppressed in order to separate targets from clutter. One manifestation of this coherent combination is in spatial domain processing, which takes the form of azimuth and elevation transmit/receive antenna beams. Another manifestation is in the time domain, in which targets in a range cell of limited size are enhanced while targets outside this range cell are suppressed. In this second form, there is a correspondence between the range cell size (resolution) and the signal bandwidth in the frequency domain. In communications applications the broader bandwidth translates into a higher data rate. In all three domains (i.e. spatial, temporal, and frequency), there will be unwanted “sidelobes” that can be sources of interference and false-targets, or data rate reductions.
The set of performance measures that determine the design of the code and the associated filters used in radar pulse compression include SNR loss, code amplitude, peak response broadening, and sidelobe behavior. The design of such codes and filters constitutes a tradeoff among the various performance measures. Optimized pulse compression search techniques have been developed that can compute many codes and filter combinations in response to each set of performance requirements. The corresponding filters can be “matched” to the codes in length/time and in amplitude and phase, so as to improve SNR gain and resolution, or “mismatched” in length/time, amplitude and phase, so as to reduce the correlation of sidelobes. Similar tradeoffs can be achieved in the design of communications signals.
One prior method of minimizing sidelobes by optimizing matched filter codes is described in “Multi-parameter Local Optimization for the Design of Superior Matched Filter Polyphase Pulse Compression Codes,” by Nunn and Welch. One benefit of using a matched filter is that it maximizes the gain in the SNR (i.e., the processing gain). Hence, the matched filter has no SNR loss because its filter characteristics are precisely matched to the received waveform. Conversely, when implementing a mismatched filter to reduce correlation side lobes, some of this SNR processing gain and/or target resolution may be lost.
Synthetic aperture radar (SAR) is a particular type of radar that uses a plurality of small, low-directivity, stationary antennas scattered near or around the target area, or an antenna moving over stationary targets. Echo waveforms received by the moving or plurality of antennas can be processed to resolve the target. In some cases, SAR radar may be improved by combining many radar pulses to form a synthetic aperture, using additional antennas or significant additional processing. SAR operation typically involves transmitting signals that cover a broad spectrum, or frequency bandwidth, to obtain desirable resolution (e.g., 200 MHz to 2 GHz). For example, in SAR applications, the bandwidth occupied by the radar is so large that it overlaps with large swaths of heavily utilized and important spectral regions. These applications tend to cause heavy in-band, and sometimes out-of-band, spectral interference. Similarly high data-rate communications involves transmitting signals that cover a broad spectrum which can overlap with bands reserved for other purposes.
The demands and prevalence of modern electronic communications, navigation, and other systems make it difficult to obtain large swaths of contiguous bandwidth. In the real world, spectrum is a precious commodity that is carefully managed. For example, it may be necessary to satisfy spectrum managers that restrict transmission frequency bandwidth. Although radar designers have developed various methods to reduce time sidelobes, they have been less successful at minimizing spectral sidelobes and in band spectral properties. As a result, many of these pulse compression codes have not been heavily utilized in real world radar systems because of their spectral shortcomings. For general pulse compression applications, these out-of-band spectral emissions can interfere with other communications or radar devices at nearby frequencies. In broadband applications, the problem is even more serious. These spectral interference problems are both time and location dependent. In the communications realm much emphasis has been placed on reducing spectral impact, but at a high cost to peak-to-average-power ratio (PAPR) and intersymbol interference.
Thus, for many applications, most notably SAR applications and high data rate communications, it is important to have available methods to rapidly and simultaneously control both the time side lobes and spectral characteristics of these transmit waveforms. Nunn has developed methods to achieve fine control over time side lobe code characteristics, such as peak sidelobe levels (PSLs) or integrated sidelobe levels (ISLs) of discrete, constant amplitude pulse compression codes using constrained optimization techniques. Nunn has also used the same methodology to create mismatched filters with excellent ISL, PSL and loss characteristics. Prior to the current effort, these methods have not been used to address the spectral issues.
The proliferation of radio-frequency devices has had two principal effects. First, it has resulted in the fragmentation of available communications spectrum due to interference sub-bands. Second, it has resulted in the imposition of varying and often extremely difficult spectral mask requirements. Prior known communications approaches have involved the use of multiple carriers that make use of disaggregated spectrum. These approaches, however, lead to very high peak-to-average power ratios (PAPR), limiting their utility in certain environments. Other techniques involve spectral filtering which imposes a cost in the form of intersymbol interference.
Demands for bandwidth continue to grow and available bandwidth is becoming increasingly disaggregated. There remains a need to maximize use of disaggregated spectrum. Specifically, this requires maximum use of spectrum under spectral mask conditions imposed at fragment edges. There is also a continuing need to be able to adapt to changing spectral conditions. Further, there remains a need to maintain or enhance data rates relative to standard BPSK under normal conditions.